Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning

TL;DR

This paper introduces a new method using EEG signals and machine learning to detect cognitive load with high accuracy, focusing on brain lobe activity.

Contribution

A novel EMFD-based optimized machine learning framework for lobe-wise cognitive load detection with high accuracy.

Findings

The EMFD-based OML framework achieved 97.8% accuracy on the MAT dataset and 96.4% on the STEW dataset.

The frontal lobe showed the highest cognitive load detection accuracy across both datasets.

The method outperforms existing approaches and is robust across different datasets.

Abstract

Cognitive load significantly affects neural activity, making its assessment important in neuroscience and human–computer interaction. EEG provides a noninvasive way to monitor brain responses to mental effort. This study explores EEG-based feature extraction and classification methods to accurately assess cognitive load during mental tasks.

EEG signals were recorded from all brain lobes over 4 seconds and decomposed into ten intrinsic mode functions using Empirical Fourier Decomposition (EMFD). Entropy-based features were extracted, and feature reduction was applied. Both lobe-wise and overall classifications were performed using optimized ensemble machine learning (OML) and conventional ML classifiers. The approach was evaluated on the Mental Arithmetic Task (MAT) and Spatial Transcriptomic Multi-View (STEW) datasets.

The proposed EMFD-based OML framework achieved high accuracy, reaching 97.8% on the MAT dataset and 96.4% on the STEW dataset. Lobe-wise analysis showed strong performance across all brain regions, with the frontal lobe achieving the highest accuracies of 97.8% (MAT) and 96.08% (STEW).

The findings demonstrate that EMFD combined with optimized ensemble learning effectively enhances EEG-based cognitive load detection. The consistent performance across datasets confirms the robustness of the method, while lobe-wise analysis highlights the frontal lobe’s key role in cognitive processing. The proposed framework outperforms existing methods and shows strong potential for real-world cognitive monitoring applications.

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